Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers
Abstract
Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To demonstrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.
- Authors:
-
[1];
[2];
[3];
[1]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- North Carolina State Univ., Raleigh, NC (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1771971
- Alternate Identifier(s):
- OSTI ID: 1963670
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Quantum Science and Technology
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 2; Journal ID: ISSN 2058-9565
- Publisher:
- IOPscience
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Citation Formats
Metcalf, Mekena, Stone, Emma, Klymko, Katherine, Kemper, Alexander F., Sarovar, Mohan, and de Jong, Wibe A. Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers. United States: N. p., 2022.
Web. doi:10.1088/2058-9565/ac546a.
Metcalf, Mekena, Stone, Emma, Klymko, Katherine, Kemper, Alexander F., Sarovar, Mohan, & de Jong, Wibe A. Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers. United States. https://doi.org/10.1088/2058-9565/ac546a
Metcalf, Mekena, Stone, Emma, Klymko, Katherine, Kemper, Alexander F., Sarovar, Mohan, and de Jong, Wibe A. Tue .
"Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers". United States. https://doi.org/10.1088/2058-9565/ac546a. https://www.osti.gov/servlets/purl/1771971.
@article{osti_1771971,
title = {Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers},
author = {Metcalf, Mekena and Stone, Emma and Klymko, Katherine and Kemper, Alexander F. and Sarovar, Mohan and de Jong, Wibe A.},
abstractNote = {Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To demonstrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.},
doi = {10.1088/2058-9565/ac546a},
journal = {Quantum Science and Technology},
number = 2,
volume = 7,
place = {United States},
year = {Tue Mar 08 00:00:00 EST 2022},
month = {Tue Mar 08 00:00:00 EST 2022}
}
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